Recently researchers have started taking fairness and discrimination into consideration in the design of personalized recommendation algorithms [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}.
Among them, [4]} was the first paper of studying fairness in classic and contextual bandits. It defined fairness with respect to one-step rewards and introduced a notion of meritocratic fairness,
i.e., the algorithm should never place higher selection probability on a less qualified arm (e.g., job applicant) than on a more qualified arm.
The following works along this direction include [5]} for infinite and contextual bandits, [6]} for reinforcement learning, [2]} for the simple stochastic bandit setting with calibration based fairness.
However, all existing works require some fairness constraint on arms at every round of the learning process, which is different from our user-side fairness setting. One recent work
[14]} focused on achieving user-side fairness in bandit setting, but it
only purposed a heuristic way to achieve correlation based group level fairness and didn't incorporate causal inference and counterfactual fairness into bandits.
